Python Training in Hyderabad

SocialPrachar provides Best Python Training In Hyderabad. Our Advanced Job Oriented Curriculum Helps Trainees to Get job easily in just 50 Days. Learn Advanced Python Training with Data Science including 2 live projects and MNC Certification. We Provide Classroom, Online, Daily, Weekend Classes on Python Programming.

In the present era the people and technology are heading to the programming languages and “PYTHON” is also one of the most using  a programming language. It is implementing and escalating by different of new technologies and many best job opportunities are getting towards that. It is also easy to learn for beginners. Before going into detail I will just demonstrate in an easy path so it will comprehend to everyone.

1. Python Programming Basics
2. Installing Jupyter Notebooks
3. Python Overview
4. Python 2.7 vs Python 3
5. Python Identifiers
6. Various Operators and Operators Precedence
7. Getting input from User,Comments,Multi line Comments.

2. Making Decisions And Loop Control
3. Simple if Statement,if-else Statement if-elif Statement.
4. Introduction To while Loops.
5. Introduction To for Loops,Using continue and break.

3. Python Data Types: List,Tuples,Dictionaries
4. Python Lists,Tuples,Dictionaries
5. Accessing Values
6. Basic Operations
7. Indexing, Slicing, and Matrixes
8. Built-in Functions & Methods
9. Exercises on List,Tuples And Dictionary

4. Functions And Modules
5. Introduction To Functions
6. Why Defining Functions
7. Calling Functions
8. Functions With Multiple Arguments.
9. Anonymous Functions – Lambda Using Built-In Modules,User-Defined Modules,Module Namespaces,Iterators And Generators.

5. File I/O And Exceptional Handling
6. Opening and Closing Files
7. open Function,file Object Attributes
8. close() Method ,Read,write,seek.Exception Handling,the try-finally Clause
9. Raising an Exceptions,User-Defined Exceptions
10. Regular Expression- Search and Replace
11. Regular Expression Modifiers
12. Regular Expression Patterns,re module.

6. Numpy
7. Introduction to Numpy. Array Creation,Printing Arrays
8. Basic Operations- Indexing, Slicing and Iterating Shape Manipulation – Changing shape,stacking and spliting of array Vector stacking

7. Pandas And Matplotlib
8. Introduction to Pandas
9. Importing data into Python
10. Pandas Data Frames,Indexing Data Frames ,Basic Operations With Data frame,Renaming Columns,Subletting and filtering a data frame.
11. Matplotlib – Introduction,plot(),Controlling
12. Line Properties,Working with Multiple Figures,Histograms

 

 UNDERSTANDING AND IMPLEMENTING MACHINE LEARNING

1. Introduction To Machine Learning
2. What is Machine Learning?
3. What is the Challenge?
4. Introduction to Supervised Learning,Unsupervised Learning
5. What is Reinforcement Learning?

 

2. Linear Regression
3. Introduction to Linear Regression
4. Linear Regression with Multiple Variables
5. Disadvantage of Linear Models
6. Interpretation of Model Outputs
7. Understanding Covariance and Colinearity
8. Understanding Heteroscedasticity

 

Case Study

• Application of Linear
• Regression for Housing Price Prediction

 

3. Logistic Regression
4. Introduction to Logistic Regression.– Why Logistic Regression .
5. Introduce the notion of classification Cost function for logistic regression
6. Application of logistic regression to multi-class classification.
7. Confusion Matrix, Odd’s Ratio And ROC Curve
8. Advantages And Disadvantages of Logistic Regression.

 

Case Study:

• To classify an email as spam or not spam using logistic Regression.

 

4. Decision Trees And Supervised Learning
5. Decision Tree – data set
6. How to build decision tree?
7. Understanding Kart Model
8. Classification Rules- Overfitting Problem
9. Stopping Criteria And Pruning
10. How to Find final size of Trees?
11. Model A decision Tree.
12. Naive Bayes
13. Random Forests and Support Vector Machines
14. Interpretation of Model Outputs

Case Study:

• Business Case Study for Kart Model
• Business Case Study for Random Forest
• Business Case Study for SVM

 

5. Unsupervised Learning
6. Hierarchical Clustering
7. k-Means algorithm for clustering – groupings
8. of unlabeled data points.
9. Principal Component Analysis(PCA)- Data
10. Independent components analysis(ICA)
11. Anomaly Detection
12. Recommender System-collaborative filtering algorithm

Case Study– Recommendation Engine for

e-commerce/retail chain

• Statistics

• Deep Learning

• NLP

Python Use Cases

Most of the people are using python and it is an interpreted language, which means the written code not actually translate to computer readable format in a run time. This type of language is called as scripting language. A large majority of web applications  rely on Python, including Google’s search engine, YouTube, and the web-oriented transaction systems. Most of the industries are using python to inflate their share markets and even NASA often uses python when are programming their equipments and space machineries.

Python can also be used to process text, display numbers or images, solve scientific equations, and save data.

BENEFITS OF  LEARNING PYTHON PROGRAMMING:

There are many benefits of learning python as it is very easy to learn. Suppose you are a beginner and you are a not aware of coding so, don’t put a back step it is can be easy to gain knowledge form clueless people also.

Python is widely used, including by a number of big companies like Google, Instagram, Disney, Yahoo!, Nokia, IBM, and many others. Not even in the IT and software field, it is also used in the electronics as in an embedded system. The Raspberry Pi – which is a mini computer and DIY lover’s dream – relies on Python as its main programming language too.

SOME BENEFITS ARE INCLUDED IN PYTHON LIKE:

1. It can develop prototypes quickly because it is so easy to work with and read.
2. As compared to Java and C languages, it will be more productive programming environment. Most professional coders had  shared their experience that they get more productive when they are working with python.
3. Python is easy to read, even if you’re not a skilled programmer. Anyone can begin working with the language, all it takes is a bit of patience and a lot of practice.
4. Most automation, data mining, and big data and data science  platforms rely on Python.